In this paper, we investigate the impact of pattern leakage during data preprocessing on the
reliability of Machine Learning (ML) based intrusion detection systems (IDS). Data leakage …

Background: The COVID-19 pandemic has reiterated the need for cohesive, collective, and
deliberate societal efforts to address inherent inefficiencies in our health systems and …

Abstract Application-layer Distributed Denial of Service (App-DDoS) attacks continue to be a
pervasive problem in cybersecurity, despite the availability of various defensive frameworks …

In the current era of information technology development, web server security has become a
primary concern in maintaining data integrity, confidentiality, and availability. With the …

In today's world, where digital threats are on the rise, one particularly concerning threat is
the Mirai botnet. This malware is designed to infect and command a collection of Internet of …

Network management is a crucial task to maintain modern systems and applications
running. Some applications have become vital for society and are expected to have zero …

Nowadays, networks have adopted the always-connected concept and as time goes on,
technology makes gadgets smaller and cheaper. This revolution lays the foundations for the …

Network intrusion detection systems (NIDS) play a pivotal role in safeguarding critical digital
infrastructures against cyber threats. Machine learning-based detection models applied in …

This paper examines the main blockchain and decentralized application features, their
advantages and disadvantages for use in message interchange systems, and proposes a …

ABSTRACT Denial of Service (DoS) attacks target the availability part of the CIA triad
(Confidentiality, Availability, and Integrity). A special category of these attacks is the …